Abstract 15659: Dynamic Device Derived Heart Failure Risk Score as a Predictor of Heart Failure Hospitalisation

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Maciej Debski ◽  
Lesley Howard ◽  
Paula Black ◽  
Angelic Goode ◽  
Christopher Cassidy ◽  
...  
Keyword(s):  
Heart Failure ◽  
Risk Score ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Tertiary Centre ◽  
Cardiac Devices ◽  
Failure Risk ◽  
Heart Failure Hospitalisation

Introduction: The number of people being admitted to hospital in England due to heart failure (HF) has risen by a third in the last five years. Implantable cardiac devices with integrated heart failure diagnostics are capable of combining daily measurements of multiple device-derived parameters and provide a heart failure risk score (HFRS) which might help predict HF worsening. Methods: Between 2015 and 2019 231 consecutive HF device patients were co-managed (CM) by specialist HF nurses in a tertiary centre. Follow-up was truncated at last device transmission in 2019. HF nurses’ interventions to alerts were recorded prospectively. HF-related hospitalisations were collected from hospital records. We analysed the predictive value of baseline variables on the count of days in high HFRS in a negative binomial regression model. The device settings: Optivol CareAlert switched ON vs OFF were compared. Results: 200 patients with CRT-D were followed up for 2.6 [1.0-2.8] years (Figure). Baseline characteristics and their effect on the incidence rate ratio (IRR) of days in high HFRS are presented in Table. A total of 3,486 transmissions were assessed, median 7.3 [5.9-10.0] per patient-year; 591 high HFRS episodes occurred in 115 (58%) pts. Optivol OFF increased the rate of high HFRS being transmitted >30 days after its end (45% vs 35%, P=0.018) and increased the time from episode start to transmission (36 [16-68] vs 24 [8-53] days, P<0.001). Of 21 hospitalisations for decompensated HF, 15 were predicted by high HFRS within 30 days whereas 6 were predated by medium HFRS. Conclusion: Patients who have not had a single high HFRS during follow-up did not need admission for decompensated HF.

Abstract 16807: Burden and Timing of Hospitalizations in Heart Failure: A Community Study

Circulation ◽  
2014 ◽  
Vol 130 (suppl_2) ◽  
Author(s):  
Alanna M Chamberlain ◽  
Yariv Gerber ◽  
Shannon M Dunlay ◽  
Sheila M Manemann ◽  
Susan A Weston ◽  
...  
Keyword(s):  
Heart Failure ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Community Study ◽  
Olmsted County ◽  
Comorbid Conditions ◽  
Binomial Regression ◽  
Time Periods ◽  
Patients Experience

Background: Heart failure (HF) patients are experiencing an epidemic of hospitalizations. Nevertheless, data on the frequency and distribution of hospitalizations over the course of the disease are lacking. Methods: We determined the rates of hospitalizations during periods of follow-up in Olmsted County, MN residents with incident HF from 2000-2010. HF was identified using ICD-9 code 428 and validated by the Framingham criteria. All hospitalizations were obtained for the 2 years following incident HF and each was categorized as due to HF, other cardiovascular (ICD-9 codes 390-427, 429-459), or non-cardiovascular causes. Follow-up was divided into discrete time periods (epochs): 0-30, 31-182, 183-365, and 366-730 days. Negative binomial regression examined associations between epochs of follow-up time and hospitalizations. Results: Among 1702 incident HF patients (mean age 76, 44% male), 1143 (67%) were hospitalized at index. Over the 2 year follow-up, 3008 hospitalizations were observed among 1136 patients, and 351 patients were hospitalized within 30 days of incident HF (median time from HF to hospitalization: 11 days). The majority of hospitalizations were due to non-cardiovascular causes (63% vs. 14% HF, 23% other cardiovascular); however, a larger proportion of HF and other cardiovascular hospitalizations were observed within the first 30 days (52% non-cardiovascular, 18% HF, 30% other cardiovascular) compared to the other time periods. The rate of hospitalization was highest within the first 30 days and was similar across sex, presentation of incident HF (inpatient, outpatient), and type of HF (preserved (≥50%), reduced (<50%) ejection fraction) (Table). Conclusions: HF patients experience high rates of hospitalizations, particularly within the first 30 days, and the majority of hospitalizations are for non-cardiovascular causes. Continued efforts to manage comorbid conditions and reduce hospitalizations in HF patients are needed.

scholarly journals Real world experience with heart failure risk status generated by cardiac resynchronisation therapy defibrillators: high heart failure risk status incidence, causes and timing of remote transmissions

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
M Debski ◽  
L Howard ◽  
P Black ◽  
A Goode ◽  
C Cassidy ◽  
...  
Keyword(s):  
Heart Failure ◽  
Risk Score ◽  
Real World ◽  
Ventricular Rate ◽  
Funding Source ◽  
Risk Status ◽  
Remote Monitoring System ◽  
Failure Risk

Abstract Background Proactive patient monitoring is of paramount importance in effective management of heart failure (HF) patients. Cardiac implantable electronic devices (CIEDs) used in HF patients are able to derive long-term trends in physiologic parameters and provide timely warning to clinicians. Little is known, however, on the real-world experience with device-generated HF risk-stratifying algorithms. Purpose Heart Failure Risk Score (HFRS) takes into account nine parameters and is calculated automatically based on long-term clinical trends. Remote transmissions provide information on the risk of HF event in next 30 days categorized as low, medium or high based on a maximum daily risk status in prior 30 days. We aimed to evaluate the ability of HFRS to alert HF specialists on the actual HF risk status. Methods The prospective registry included all patients with CIEDs featuring integrated Heart Failure Risk Score (HFRS) followed via Medtronic CareLink remote monitoring system and enabled for Co-management (CM) from May 2015 to August 2019 in a tertiary centre. High HFRS does not trigger automatic alert transmission. Study follow-up spanned between start of CM and last transmission in 2019. Inclusion criteria were CRT-D in situ, active Home Monitor, switched on OptiVol 2.0 remote alert and transmission data available on CareLink following study period completion. Transmissions were scheduled 3-monthly. Results Out of 229 consecutive patients, 132 met study criteria. Mean age was 74±10 years, 18% were female. Median follow-up duration was 2.7 years (IQR 1.3). Total number of transmissions was 2652, median per patient was 18 (IQR 13); scheduled, unscheduled and care alerts constituted 42%, 44% and 14%, respectively. One third of transmissions were automatically sent for CM review. There were 398 high HFRS episodes. OptiVol fluid index was below the threshold throughout 128 (32%) episodes. Missed episodes (not transmitted within 30 days from the final day of high HFRS) amounted to 130 (33%) and the reasons behind this included OptiVol alerting before the first day of high HFRS or persistently elevated when HFRS changed from low/medium to high (52%), low OptiVol index during the episode (38%) or other (10%). Median duration of high HFRS was 7 days (IQR 12, range, 1–187). Among timely picked-up high HFRS episodes, 38% were transmitted during the relevant episode and 62% afterwards with median delay of 10 days (IQR 15) from the last day of high HFRS; 21% of transmissions showing high HFRS were not highlighted for CM review which correlated with low OptiVol index, P&lt;0.001. The factors contributing to high HFRS included: raised OptiVol (60%), patient activity (83%), AT/AF (46%), ventricular rate (VR) during AF (6%), % of VP (40%), shocks (2%), treated VT/VF (2%), night VR (72%) and HR variability (34%). Conclusions In a real-world clinical setting high HFRS was frequently under-reported. The investigation into clinical implications is warranted. Funding Acknowledgement Type of funding source: Private grant(s) and/or Sponsorship. Main funding source(s): Our department has benefited from unrestricted grants from Boston Scientific and Medtronic Inc during the last 5 years.

P1759Elevated NT-pro BNP predicts Heart Failure re-hospitalizations after an acute coronary syndrome

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
E Martinez Rey-Ranal ◽  
A Cordero ◽  
M J Moreno ◽  
V Bertomeu Gonzalez ◽  
J Moreno Arribas ◽  
...  
Keyword(s):  
Heart Failure ◽  
Acute Coronary Syndrome ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Predictive Capacity ◽  
Coronary Syndrome ◽  
All Cause Mortality ◽  
A Prospective Study ◽  
Grace Score

Abstract Background NT pro-BNP is a well-established biomarker of tissue congestion and has prognostic value in patients with heart failure (HF) and, also, with acute coronary syndrome (ACS). Nonetheless, there is scarce evidence on the predictive capacity of NT pro-BNP for HF re-admission after an ACS. Objective To test whether elevated values of NT pro-BNP can predict subsequent hospitalizations for HF in patients discharged after an ACS. Methods We performed a prospective study of all patients discharged after an ACS in a single center. HF re-admission was analysed by competing risk regression, taking all-cause mortality as a competing event, and results are presented as sub-Hazard Ratio (sHR); recurrent hospitalizations were tested by negative binomial regression and results are presented as incidence risk ratio (IRR). Results We included 1,679 patients, mean age 70.1 (29.7) year, 71.9% males, 41.4% STEMI and mean GRACE score 151.7 (44.4). Median NT pro-BNP was 948.2 pg/ml (IQ range 274.5–2923) and patients were divided in <300U (27.0%), 300–600 pg/ml (13.4%), 600–1000 pg/ml (10.8%) and >1000 pg/ml (46.7%) A total of 132 (5.9%) died within hospitalization and follow-up was available 98% of the patients, with a median follow-up of 33 months (IQ range 16–59). A total of 220 patients (13.1%) had at least one hospital re-admission of HF and 126 (7.5%) had more than one re-hospitalization for HF. Patients with NT pro-BNP had higher un-adjusted HF re-admissions (22.2% vs. 4.4%; p<0.01). Cardiovascular mortality increased significantly in each category of NT pro-BNP (3.8%; 8.0%; 7.7%; 18.5%) as well as all-cause mortality (0.1%; 12.4%; 11.6%; 25.3%), first HF readmission (2.7%; 7.1%; 5.5%; 23.5%); patients with NT pro-BNP had higher rates of recurrent HF readmissions: 11.6/1000 vs. 2.4/1000 patients/years (p<0.01). Multivariate analyses, adjusted by age, gender, GRACE score, left ventricle ejection fraction, revascularization and medical treatments at discharge, identified that NT pro-BNP >1000 pg/ml was associated to HF re-hospitalization (sHR: 2.60 95% CI 1.12–5.95) and recurrent hospitalizations (IRR: 1.10 95% CI 1.04–1.14). Conclusions NT pro-BNP >1000 pg/ml is an accurate risk factor for first and recurrent HF rehospitalisations after an ACS.

P3543Rehospitalization burden in heart failure with mid-range ejection fraction and morbidity burden. Is it a distinct phenotype?

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
E Santas Olmeda ◽  
R De La Espriella ◽  
G Minana ◽  
E Valero ◽  
P Palau ◽  
...  
Keyword(s):  
Heart Failure ◽  
Confidence Interval ◽  
Ejection Fraction ◽  
Recurrent Events ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Morbidity Burden ◽  
Binomial Regression ◽  
Similar Risk

Abstract Heart failure with mid-range ejection fraction (HFmrEF) has been recognized as a distinct HF phenotype, but wether patients on this category fare worse, similarly, or better than those with HF with reduced EF (HFrEF) or preserved EF (HFpEF) in terms of rehospitalization risk over time remains unclear. We therefore sought to characterize the mordibity burden of HFmrEF patients by evaluating the risk of recurrent hospitalizations following an admission for acute HF. Methods We prospectively included 2,961 consecutive patients discharged for acute HF in our institution from 2004 to 2017. Patients were categorized according to their ejection fraction (EF) obtained by an echocardiography during the index admission: HFmrEF (EF 41–49%), HFrEF (EF≤40%) and HFpEF (EF≥50%). Negative binomial regression method was used to evaluate the association between EF status and recurrent all-cause and HF-related admissions. Risk estimates were expressed as incidence ratio ratios (IRR). Results Mean age of the cohort was 73.9±11.1 years, 49% were women, and 46.0% had suffered from previous HF admissions. 472 patients (15.9%) had HFmrEF, 956 (32.3%) had HFrEF, and 1,533 (51.8%) had HFpEF. At a median (interquartile range) follow-up of 2.4 (4.4) years, 1,821 (61.5%) patients died and 6,035 all-cause readmissions were registered in 2,026 patients (68.4%), being 2,163 of them HF-related. Rates of all-cause readmission per 100 patients-years of follow-up were 43.4, 47.1 and 50.1 per HFrEF, HFmrEF and HFpEF categories, respectively. After multivariable adjustment, and compared to patients with HFrEF, HFmrEF status was not associated with a higher risk of all-cause or HF-related recurrent admissions (IRR=1.06; 95% confidence interval (CI), 0.93–1.20; p=0.89), and IRR=1.07; 95% CI, 0.91–1.26; p=0.389, respectively), whereas HFpEF status was associated with a non-significant increase in the risk of all-cause recurrent admissions but a similar risk of HF-related readmissions (IRR=1.10; 95% confidence interval (CI), 0.99–1.22; p=0.06, and IRR=1.01; 95% CI, 0.88–1.16; p=0.900, respectively) Conclusion Following an admission for acute HF, patients with HFmrEF have a similar all-cause and HF-related rehospitalization burden when compared to patients with HFrEF, by means of recurrent events analysis.

scholarly journals Majority of New Onset of Dental Caries Occurred from Caries-Free Students: A Longitudinal Study in Primary School Students

2020 ◽  
Vol 17 (22) ◽  
pp. 8476
Author(s):  
Taro Kusama ◽  
Hidemi Todoriki ◽  
Ken Osaka ◽  
Jun Aida
Keyword(s):  
Longitudinal Study ◽  
Primary Schools ◽  
Negative Binomial ◽  
Predictor Variable ◽  
Negative Binomial Regression ◽  
Outcome Variable ◽  
Negative Binomial Regression Model ◽  
School Students ◽  
Carious Teeth

We examined Rose’s axiom that a large number of people exposed to a small risk may generate more cases than a small number exposed to a high risk, using data on caries incidence. This longitudinal study was based on the records of annual dental checks conducted in primary schools in Okinawa, Japan. Participants were students aged 6–11 years at baseline in 2014, and a follow-up survey was conducted after one-year. The outcome variable was the increased number of decayed, missing, and filled teeth (DMFT). The predictor variable was the baseline DMFT score. Gender, grade, and affiliated school variables were adjusted. A negative binomial regression model was used to obtain the estimated increase of DMFT score. Among 1542 students, 1138 (73.8%) were caries-free at baseline. A total of 317 (20.6%) developed new caries during the follow-up. The predicted number of new carious teeth in a caries-free students and students with DMFT = 1 at baseline were 0.26 (95% CI, 0.22–0.31) and 0.45 teeth (95% CI, 0.33–0.56), respectively. However, among the total of 502 newly onset of carious teeth, 300 teeth (59.7%) occurred from the caries-free students at baseline. Hence, prevention strategies should target the low-risk group because they comprise the majority of the population.

External validation of a heart failure risk prediction model in a remote monitoring cohort submitted to cardiac resynchronization therapy

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
I Cardoso ◽  
M Coutinho ◽  
G Portugal ◽  
A Valentim ◽  
A.S Delgado ◽  
...  
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
High Risk ◽  
Remote Monitoring ◽  
Hospital Admissions ◽  
External Validation ◽  
Left Ventricular Ejection ◽  
National Database ◽  
Failure Risk

Abstract Background Patients (P) submitted to cardiac ressynchronization therapy (CRT) are at high risk of heart failure (HF) events during follow-up. Continuous analysis of various physiological parameters, as reported by remote monitoring (RM), can contribute to point out incident HF admissions. Tailored evaluation, including multi-parameter modelling, may further increase the accuracy of such algorithms. Purpose Independent external validation of a commercially available algorithm (“Heart Failure Risk Status” HFRS, Medtronic, MN USA) in a cohort submitted to CRT implantation in a tertiary center. Methods Consecutive P submitted to CRT implantation between January 2013 and September 2019 who had regular RM transmissions were included. The HFRS algorithm includes OptiVol (Medtronic Plc., MN, USA), patient activity, night heart rate (NHR), heart rate variability (HRV), percentage of CRT pacing, atrial tachycardia/atrial fibrillation (AT/AF) burden, ventricular rate during AT/AF (VRAF), and detected arrhythmia episodes/therapy delivered. P were classified as low, medium or high risk. Hospital admissions were systematically assessed by use of a national database (“Plataforma de Dados de Saúde”). Accuracy of the HFRS algorithm was evaluated by random effects logistic regression for the outcome of unplanned hospital admission for HF in the 30 days following each transmission episode. Results 1108 transmissions of 35 CRT P, corresponding to 94 patient-years were assessed. Mean follow-up was 2.7 yrs. At implant, age was 67.6±9.8 yrs, left ventricular ejection fraction 28±7.8%, BNP 156.6±292.8 and NYHA class &gt;II in 46% of the P. Hospital admissions for HF were observed within 30 days in 9 transmissions. Stepwise increase in HFRS was significantly associated with higher risk of HF admission (odds ratio 12.7, CI 3.2–51.5). HFRS had good discrimination for HF events with receiving-operator curve AUC 0.812. Conclusions HFRS was significantly associated with incident HF admissions in a high-risk cohort. Prospective use of this algorithm may help guide HF therapy in CRT recipients. Funding Acknowledgement Type of funding source: None

scholarly journals External assessment of the EUROMACS right-sided heart failure risk score

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hirak Shah ◽  
Thomas Murray ◽  
Jessica Schultz ◽  
Ranjit John ◽  
Cindy M. Martin ◽  
...  
Keyword(s):  
Heart Failure ◽  
Right Ventricular ◽  
Risk Score ◽  
Pressure Ratio ◽  
Area Under The Curve ◽  
Left Ventricular ◽  
Right Atrial ◽  
Risk Scores ◽  
External Assessment ◽  
Failure Risk

AbstractThe EUROMACS Right-Sided Heart Failure Risk Score was developed to predict right ventricular failure (RVF) after left ventricular assist device (LVAD) placement. The predictive ability of the EUROMACS score has not been tested in other cohorts. We performed a single center analysis of a continuous-flow (CF) LVAD cohort (n = 254) where we calculated EUROMACS risk scores and assessed for right ventricular heart failure after LVAD implantation. Thirty-nine percent of patients (100/254) had post-operative RVF, of which 9% (23/254) required prolonged inotropic support and 5% (12/254) required RVAD placement. For patients who developed RVF after LVAD implantation, there was a 45% increase in the hazards of death on LVAD support (HR 1.45, 95% CI 0.98–2.2, p = 0.066). Two variables in the EUROMACS score (Hemoglobin and Right Atrial Pressure to Pulmonary Capillary Wedge Pressure ratio) were not predictive of RVF in our cohort. Overall, the EUROMACS score had poor external discrimination in our cohort with area under the curve of 58% (95% CI 52–66%). Further work is necessary to enhance our ability to predict RVF after LVAD implantation.

scholarly journals Association between fatality rate of COVID-19 and selenium deficiency in China

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hai-Yang Zhang ◽  
An-Ran Zhang ◽  
Qing-Bin Lu ◽  
Xiao-Ai Zhang ◽  
Zhi-Jie Zhang ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Viral Infections ◽  
Case Fatality ◽  
Selenium Deficiency ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Fatality Rate ◽  
Selenium Content ◽  
Individual Level ◽  
Binomial Regression

Abstract Background COVID-19 has impacted populations around the world, with the fatality rate varying dramatically across countries. Selenium, as one of the important micronutrients implicated in viral infections, was suggested to play roles. Methods An ecological study was performed to assess the association between the COVID-19 related fatality and the selenium content both from crops and topsoil, in China. Results Totally, 14,045 COVID-19 cases were reported from 147 cities during 8 December 2019–13 December 2020 were included. Based on selenium content in crops, the case fatality rates (CFRs) gradually increased from 1.17% in non-selenium-deficient areas, to 1.28% in moderate-selenium-deficient areas, and further to 3.16% in severe-selenium-deficient areas (P = 0.002). Based on selenium content in topsoil, the CFRs gradually increased from 0.76% in non-selenium-deficient areas, to 1.70% in moderate-selenium-deficient areas, and further to 1.85% in severe-selenium-deficient areas (P < 0.001). The zero-inflated negative binomial regression model showed a significantly higher fatality risk in cities with severe-selenium-deficient selenium content in crops than non-selenium-deficient cities, with incidence rate ratio (IRR) of 3.88 (95% CIs: 1.21–12.52), which was further confirmed by regression fitting the association between CFR of COVID-19 and selenium content in topsoil, with the IRR of 2.38 (95% CIs: 1.14–4.98) for moderate-selenium-deficient cities and 3.06 (1.49–6.27) for severe-selenium-deficient cities. Conclusions Regional selenium deficiency might be related to an increased CFR of COVID-19. Future studies are needed to explore the associations between selenium status and disease outcome at individual-level.

scholarly journals Statistical models for analyzing count data: predictors of length of stay among HIV patients in Portugal using a multilevel model

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ahmed Nabil Shaaban ◽  
Bárbara Peleteiro ◽  
Maria Rosario O. Martins
Keyword(s):  
Length Of Stay ◽  
Regression Model ◽  
Random Effects ◽  
Count Data ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Comprehensive Approach ◽  
Negative Binomial Regression Model ◽  
Hiv Patients ◽  
Binomial Regression

Abstract Background This study offers a comprehensive approach to precisely analyze the complexly distributed length of stay among HIV admissions in Portugal. Objective To provide an illustration of statistical techniques for analysing count data using longitudinal predictors of length of stay among HIV hospitalizations in Portugal. Method Registered discharges in the Portuguese National Health Service (NHS) facilities Between January 2009 and December 2017, a total of 26,505 classified under Major Diagnostic Category (MDC) created for patients with HIV infection, with HIV/AIDS as a main or secondary cause of admission, were used to predict length of stay among HIV hospitalizations in Portugal. Several strategies were applied to select the best count fit model that includes the Poisson regression model, zero-inflated Poisson, the negative binomial regression model, and zero-inflated negative binomial regression model. A random hospital effects term has been incorporated into the negative binomial model to examine the dependence between observations within the same hospital. A multivariable analysis has been performed to assess the effect of covariates on length of stay. Results The median length of stay in our study was 11 days (interquartile range: 6–22). Statistical comparisons among the count models revealed that the random-effects negative binomial models provided the best fit with observed data. Admissions among males or admissions associated with TB infection, pneumocystis, cytomegalovirus, candidiasis, toxoplasmosis, or mycobacterium disease exhibit a highly significant increase in length of stay. Perfect trends were observed in which a higher number of diagnoses or procedures lead to significantly higher length of stay. The random-effects term included in our model and refers to unexplained factors specific to each hospital revealed obvious differences in quality among the hospitals included in our study. Conclusions This study provides a comprehensive approach to address unique problems associated with the prediction of length of stay among HIV patients in Portugal.

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